AI-Driven Architecture Reconnaissance Workflow

An AI-Driven Architecture Reconnaissance Workflow is an automated AI-driven architecture discovery workflow that systematically uncovers software system structures through iterative AI analysis.

• AKA: AI Architecture Discovery Process, Automated Architecture Exploration, AI-Powered System Reconnaissance.
• Context:
  • It can typically execute Entry Point Discovery through AI-driven API scanning and AI-driven controller identification.
  • It can typically perform Layer Identification through AI-driven pattern matching and AI-driven responsibility clustering.
  • It can typically conduct Boundary Detection through AI-driven interface analysis and AI-driven contract extraction.
  • It can typically achieve Pattern Recognition through AI-driven architecture style detection and AI-driven design pattern identification.
  • It can typically complete Component Mapping through AI-driven module classification and AI-driven service discovery.
  • ...
  • It can often generate Architecture Hypothesises through AI-driven structure inference and AI-driven pattern synthesis.
  • It can often validate Architecture Assumptions through AI-driven consistency checking and AI-driven anomaly detection.
  • It can often produce Confidence Scores through AI-driven certainty assessment and AI-driven evidence weighting.
  • It can often iterate Discovery Refinements through AI-driven feedback loops and AI-driven precision improvement.
  • ...
  • It can range from being a Shallow AI-Driven Architecture Reconnaissance to being a Deep AI-Driven Architecture Reconnaissance, depending on its AI-driven exploration depth.
  • It can range from being a Guided AI-Driven Architecture Reconnaissance to being an Autonomous AI-Driven Architecture Reconnaissance, depending on its AI-driven human interaction.
  • ...
  • It can integrate with Software Architecture Documentation for automated documentation update.
  • It can support Architecture Modernization Projects through current state discovery.
  • It can enable Technical Due Diligence by revealing system complexity.
  • ...
• Example(s):
  • Legacy System Reconnaissance Workflows, such as:
    • COBOL Architecture Discovery uncovering mainframe module structures and batch processing flows.
    • Undocumented System Mapping revealing hidden integration points and shadow IT components.
  • Microservice Architecture Discoverys, such as:
    • Service Mesh Exploration identifying service topologys and communication protocols.
    • Event-Driven Architecture Mapping tracing event flows and saga patterns.
  • Cloud Architecture Reconnaissances, such as:
    • Serverless Function Mapping discovering lambda compositions and trigger relationships.
    • Container Orchestration Discovery revealing pod architectures and service mesh configurations.
  • ...
• Counter-Example(s):
  • Manual Architecture Review, which relies on document analysis and stakeholder interviews.
  • Static Diagram Generation, which creates visual representations without discovery process.
  • Code Documentation Tool, which extracts inline comments without architecture inference.
• See: Software Architecture Discovery, AI-Driven Workflow, Architecture Analysis Task, System Reconnaissance, Reverse Engineering.

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